Training device, state prediction device, training method, and program

Abstract

The training device 1X mainly includes a type determination means 16X and a training means 17X. The type determination means 16X determines a type of each of subjects related to an action based on a recommended action, a type of the action, or the combination thereof, and an actual action, a type of the action, or the combination thereof. The training means 17X trains a state prediction model, for each type related to the action, based on a lifelog of each of the subjects classified by the type, wherein the state prediction model is obtained by performing machine learning of a relationship between a lifelog and a predicted index value of a state to be predicted of a person for which the lifelog has been measured. The prediction result output by the state prediction model is used for decision making, for example

1 . A training device comprising:
 at least one memory configured to store instructions; and   at least one processor configured to execute the instructions to:   determine a type of each of subjects related to an action, based on
 a recommended action, which is an amount of the action recommended to each of the subjects, a type of the action, or a combination of the amount of the action and the type of the action, and 
 an actual action, which is an actual amount of the action of each of the subjects, a type of the action, or a combination of the actual amount of the action and the type of the action; and 
   train a state prediction model, for each type related to the action, based on a lifelog of each of the subjects classified by the type, wherein   the state prediction model is obtained by performing machine learning of a relationship between a lifelog and a predicted index value of a state to be predicted of a person for which the lifelog has been measured.   
     
     
         2 . The training device according to  claim 1 , wherein the at least one processor is configured to execute the instructions to determine whether each of the subjects has complied with the recommended action based on the recommended action and the actual action, and determines the type based on a result of the determination as to whether each of the subjects has complied with the recommended action. 
     
     
         3 . The training device according to  claim 2 , wherein the at least one processor is configured to execute the instructions to further determine at least one of a gender or an age of each of the subjects, and determine the type based on the result of the determination as to whether each of the subjects has complied with the recommended action and a result of the determination as to at least one of the gender or the age. 
     
     
         4 . The training device according to  claim 1 , wherein the at least one processor is configured to execute the instructions to train a plurality of the state prediction models having a different length from a prediction time point at which the state is predicted using the state prediction model to a predicted time point at which the state is predicted. 
     
     
         5 . A state prediction device comprising:
 at least one memory configured to store instructions; and   at least one processor configured to execute the instructions to:   determine a type of a subject related to an action, based on
 a recommended action, which is an amount of the action recommended to the subject, a type of the action, or a combination of the amount of the action and the type of the action, and 
 an actual action, which is an actual amount of the action of the subject, a type of the action, or a combination of the actual amount of the action and the type of the action; 
   select a state prediction model to be used in state prediction of the subject from state prediction models trained for each type related to the action based on the determined type; and   predict a state of the subject based on the selected state prediction model and a lifelog of the subject, wherein   the state prediction model is obtained by performing machine learning of a relationship between a lifelog and a predicted index value of a state to be predicted of a person for which the lifelog has been measured.   
     
     
         6 . The state prediction device according to  claim 5 , wherein the at least one processor is configured to execute the instructions to further calculate the recommended action to be newly recommended to the subject based on the lifelog of the subject. 
     
     
         7 . The state prediction device according to  claim 6 , wherein the at least one processor is configured to execute the instructions to further output, using an output device, a result of the prediction of the state and the recommended action. 
     
     
         8 . The state prediction device according to  claim 5 , the at least one processor is configured to execute the instructions to further obtain the lifelog of the subject in a predetermined period immediately before a prediction time point, wherein
 the at least one processor is configured to execute the instructions to generate a lifelog from the prediction time point to a predicted time point at which the state is predicted based on the lifelog in the predetermined period, and predicts the state of the subject based on the lifelog in the predetermined period, the lifelog from the prediction time point to the predicted time point, and the state prediction model.   
     
     
         9 . A training method executed by a computer, comprising:
 determining a type of each of subjects related to an action, based on
 a recommended action, which is an amount of the action recommended to each of the subjects, a type of the action, or a combination of the amount of the action and the type of the action, and 
 an actual action, which is an actual amount of the action of each of the subjects, a type of the action, or a combination of the actual amount of the action and the type of the action; and 
   training a state prediction model, for each type related to the action, based on a lifelog of each of the subjects classified by the type, wherein   the state prediction model is obtained by performing machine learning of a relationship between a lifelog and a predicted index value of a state to be predicted of a person for which the lifelog has been measured.